I am currently working on the foundations
of machine learning and the application of evolutionary
algorithms to Real World learning problems. The search
for foundations of machine learning leads to three main questions:
How should a learning system represent and process uncertain
information, or, what is the proper inductive logic?
What set of possible models should the system consider?
How to relate the evidence for a
model to its complexity?
In the long run, a general learning system
should be able to detect as many regularities in its percept stream as
possible, while dealing sensibly with the inherent uncertainty of predictions
based on a finite amount of data.
Reports please send
to jz@iai.uni-bonn.de by Friday, 7 March 2014 at
the latest. Group reports are possible, but maximally 4 students per
group are allowed.
Reports for week 2 please send to jz@iai.uni-bonn.de by Friday, October 25 at
the latest. Group reports are possible, but maximally 4 students per
group are allowed.
Summer Lecture 2013: Analysis of Microarray Data with Methods from Machine Learning and Network Theory
Participants of the WNA 2011-Workshop (from left to right): Yupeng Cun, Ashutosh
Malhotra, Paurush Praveen, Mikael Gast, Mufassra Naz, Seraya Maouche, Steve
Horvath, Khalid Abnaof, Katrin Illner, Jörg Zimmermann.
From Theory to Practice: An Evolutionary Algorithm for the Antenna
Placement Problem
S. Tsutsui, A. Ghosh (Eds.): Advances in Evolutionary Computation,
pp. 713-737, Springer, 2003
Jörg Zimmermann, Robin Höns, Heinz Mühlenbein:
ENCON: An Evolutionary Algorithm for the Antenna Placement Problem Computers & Industrial Engineering, 44(2): 209-226, 2003
Frank Schweitzer, Jörg Zimmermann, Heinz Mühlenbein:
Communication and Self-Organisation in Complex Systems: A Basic Approach
M. M. Fischer, J. Fröhlich (Eds.): Knowledge, Complexity and Innovation
Systems, pp. 275-296, Springer, 2001